R Notebook

Import Dataset and create working dataset:

#install.packages("plotly")
library(plotly)
library(reshape2)
library(tidyverse)
games <- read.csv("../../data/vgsales.csv")
game_melt <- melt(data=games,id.vars = c("Rank","Name","Platform","Year","Genre","Publisher"),measure.vars=c("NA_Sales","EU_Sales","JP_Sales","Other_Sales","Global_Sales"))
game_melt$Year <- as.integer(game_melt$Year)

NAs introduced by coercion

colnames(game_melt)[7] <- "Region"
colnames(game_melt)[8] <- "Copies Sold"
head(game_melt)

seq(1970,2017,step=5)

In seq.default(1970, 2017, step = 5) :
 extra argument 㤼㸱step㤼㸲 will be disregarded

 [1] 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
[14] 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
[27] 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
[40] 2009 2010 2011 2012 2013 2014 2015 2016 2017

Graph time baby!

#Number of Copies Sold
graph1 <- game_melt %>% 
    filter(Region == "NA_Sales") %>%
    group_by(Region,Year,Genre) %>%
    summarise("Copies Sold" = sum(`Copies Sold`)) %>% 
    ggplot() +
    aes(x=Year,
        y=`Copies Sold`,
        fill = Genre) + 
    geom_bar(stat="identity")+
    theme(axis.text.x = element_text(angle = 90, hjust=0.95, vjust=0.2)) +
    ylab("Number of Copies Sold (in millions)")

`summarise()` regrouping output by 'Region', 'Year' (override with `.groups` argument)

    
ggplotly(graph1)

Removed 12 rows containing missing values (position_stack).

#Number Releases
graph2 <- game_melt %>% 
    filter(Region == "NA_Sales") %>%
    group_by(Region,Year,Genre) %>%
    count(Year,Genre) %>%
    rename(`Number of Releases`="n")%>% 
    ggplot() +
    aes(x=Year,
        y=`Number of Releases`,
        fill = Genre) + 
    geom_bar(stat="identity")+
    theme(axis.text.x = element_text(angle = 90, hjust=0.95, vjust=0.2))+
    ylab("Number of Games Released")
ggplotly(graph2)

```r
#Number of Platforms, Genres and Publishers with games > 100 copies sold 
graph3 <- game_melt %>% 
    filter(Region == \JP_Sales\) %>%
    group_by(Year)%>%
    melt(id.vars=c(\Year\),measure.vars=c(\Genre\,\Platform\,\Publisher\)) %>%
    rename(Category='variable') %>% 
    group_by(Year,Category) %>%
    unique() %>%
    count(Year,Category) %>%
    rename(`Counts of Genres, Publishers and Platforms`= n) %>% 
    ggplot() +
    aes(x=Year,
        y=`Counts of Genres, Publishers and Platforms`,
        fill = Category) + 
    geom_bar(stat=\identity\)+
    theme(axis.text.x = element_text(angle = 90, hjust=0.95, vjust=0.2))+
    ylab(\Counts of Genres

testfxc <-function(reg,plat,gen,pub,years) {
         # Input: List of Regions, Platforms, Genres, Publishers, Min and Max Year
         # Output: Graph
         #
         # Create subset based on filters 
         # Pass to graph
         # Output graph

        filter_region = reg
        
         if (plat != "all"){
             filter_plat = plat
         } else {
             filter_plat = unique(game_melt$Platform)
         }
         if (gen != "all"){
             filter_gen = gen
         } else {
             filter_gen = unique(game_melt$Genre)
         }
         if (pub != "all"){
             filter_pub = pub
         } else {
             filter_pub = unique(game_melt$Publisher)
         }
         min_year = years[1]
         max_year = years[2]
         
         graph1 <- game_melt[,2:8] %>% 
             subset(Region %in% filter_region, Platform %in% filter_plat, Genre %in% filter_gen, Publisher %in% filter_pub, Year >= min_year | Year <= max_year) %>%
             group_by(Region,Year,Genre) %>%
             summarise("Copies Sold" = sum(`Copies Sold`)) %>% 
             ggplot() +
             aes(x=Year,
                 y=`Copies Sold`,
                 fill = Genre) + 
             geom_bar(stat="identity")+
             theme(axis.text.x = element_text(angle = 90, hjust=0.95, vjust=0.2)) +
             ylab("Number of Copies Sold (in millions)")
         ggplotly(graph1)
    }

filter_region = list("JP_Sales")
filter_plat = unique(game_melt$Platform)

filter_gen = unique(game_melt$Genre)

filter_pub = unique(game_melt$Publisher)

min_year = 1980
max_year = 2017
 graph1 <- game_melt %>% 
     subset(Region %in% filter_region & Platform %in% filter_plat & Genre %in% filter_gen & Publisher %in% filter_pub & Year >= min_year & Year <= max_year) %>%
     group_by(Year,Genre) %>%
     summarise("Copies Sold" = sum(`Copies Sold`)) %>% 
     ggplot() +
     aes(x=Year,
         y=`Copies Sold`,
         fill = Genre) + 
     geom_bar(stat="identity")+
     theme(axis.text.x = element_text(angle = 90, hjust=0.95, vjust=0.2)) +
     ylab("Number of Copies Sold (in millions)")

`summarise()` regrouping output by 'Year' (override with `.groups` argument)

 ggplotly(graph1)

}

Error: unexpected '}' in "}"

game_melt$Year > 2000

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